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http://dx.doi.org/10.3745/KIPSTB.2002.9B.3.327

Multiple Texture Image Recognition with Unsupervised Block-based Clustering  

Lee, Woo-Beom (Dept.of Computer Engineering, Daegu Technology College)
Kim, Wook-Hyun (Dept.of Electronics Information Engineering, Yeungnam University)
Publication Information
The KIPS Transactions:PartB / v.9B, no.3, 2002 , pp. 327-336 More about this Journal
Abstract
Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.
Keywords
Texture analysis; Orientation-field Feature; Self-organization neural network; Unsupervised clustering; Texture Image segmentation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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